{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Initialization\n",
    "\n",
    "Welcome to the first assignment of \"Improving Deep Neural Networks\". \n",
    "\n",
    "Training your neural network requires specifying an initial value of the weights. A well chosen initialization method will help learning.  \n",
    "\n",
    "If you completed the previous course of this specialization, you probably followed our instructions for weight initialization, and it has worked out so far. But how do you choose the initialization for a new neural network? In this notebook, you will see how different initializations lead to different results. \n",
    "\n",
    "A well chosen initialization can:\n",
    "- Speed up the convergence of gradient descent\n",
    "- Increase the odds of gradient descent converging to a lower training (and generalization) error \n",
    "\n",
    "To get started, run the following cell to load the packages and the planar dataset you will try to classify."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "import sklearn.datasets\n",
    "from init_utils import sigmoid, relu, compute_loss, forward_propagation, backward_propagation\n",
    "from init_utils import update_parameters, predict, load_dataset, plot_decision_boundary, predict_dec\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (7.0, 4.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# load image dataset: blue/red dots in circles\n",
    "train_X, train_Y, test_X, test_Y = load_dataset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You would like a classifier to separate the blue dots from the red dots."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 - Neural Network model "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You will use a 3-layer neural network (already implemented for you). Here are the initialization methods you will experiment with:  \n",
    "- *Zeros initialization* --  setting `initialization = \"zeros\"` in the input argument.\n",
    "- *Random initialization* -- setting `initialization = \"random\"` in the input argument. This initializes the weights to large random values.  \n",
    "- *He initialization* -- setting `initialization = \"he\"` in the input argument. This initializes the weights to random values scaled according to a paper by He et al., 2015. \n",
    "\n",
    "**Instructions**: Please quickly read over the code below, and run it. In the next part you will implement the three initialization methods that this `model()` calls."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model(X, Y, learning_rate = 0.01, num_iterations = 15000, print_cost = True, initialization = \"he\"):\n",
    "    \"\"\"\n",
    "    Implements a three-layer neural network: LINEAR->RELU->LINEAR->RELU->LINEAR->SIGMOID.\n",
    "    \n",
    "    Arguments:\n",
    "    X -- input data, of shape (2, number of examples)\n",
    "    Y -- true \"label\" vector (containing 0 for red dots; 1 for blue dots), of shape (1, number of examples)\n",
    "    learning_rate -- learning rate for gradient descent \n",
    "    num_iterations -- number of iterations to run gradient descent\n",
    "    print_cost -- if True, print the cost every 1000 iterations\n",
    "    initialization -- flag to choose which initialization to use (\"zeros\",\"random\" or \"he\")\n",
    "    \n",
    "    Returns:\n",
    "    parameters -- parameters learnt by the model\n",
    "    \"\"\"\n",
    "        \n",
    "    grads = {}\n",
    "    costs = [] # to keep track of the loss\n",
    "    m = X.shape[1] # number of examples\n",
    "    layers_dims = [X.shape[0], 10, 5, 1]\n",
    "    \n",
    "    # Initialize parameters dictionary.\n",
    "    if initialization == \"zeros\":\n",
    "        parameters = initialize_parameters_zeros(layers_dims)\n",
    "    elif initialization == \"random\":\n",
    "        parameters = initialize_parameters_random(layers_dims)\n",
    "    elif initialization == \"he\":\n",
    "        parameters = initialize_parameters_he(layers_dims)\n",
    "\n",
    "    # Loop (gradient descent)\n",
    "\n",
    "    for i in range(0, num_iterations):\n",
    "\n",
    "        # Forward propagation: LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SIGMOID.\n",
    "        a3, cache = forward_propagation(X, parameters)\n",
    "        \n",
    "        # Loss\n",
    "        cost = compute_loss(a3, Y)\n",
    "\n",
    "        # Backward propagation.\n",
    "        grads = backward_propagation(X, Y, cache)\n",
    "        \n",
    "        # Update parameters.\n",
    "        parameters = update_parameters(parameters, grads, learning_rate)\n",
    "        \n",
    "        # Print the loss every 1000 iterations\n",
    "        if print_cost and i % 1000 == 0:\n",
    "            print(\"Cost after iteration {}: {}\".format(i, cost))\n",
    "            costs.append(cost)\n",
    "            \n",
    "    # plot the loss\n",
    "    plt.plot(costs)\n",
    "    plt.ylabel('cost')\n",
    "    plt.xlabel('iterations (per hundreds)')\n",
    "    plt.title(\"Learning rate =\" + str(learning_rate))\n",
    "    plt.show()\n",
    "    \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 - Zero initialization\n",
    "\n",
    "There are two types of parameters to initialize in a neural network:\n",
    "- the weight matrices $(W^{[1]}, W^{[2]}, W^{[3]}, ..., W^{[L-1]}, W^{[L]})$\n",
    "- the bias vectors $(b^{[1]}, b^{[2]}, b^{[3]}, ..., b^{[L-1]}, b^{[L]})$\n",
    "\n",
    "**Exercise**: Implement the following function to initialize all parameters to zeros. You'll see later that this does not work well since it fails to \"break symmetry\", but lets try it anyway and see what happens. Use np.zeros((..,..)) with the correct shapes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: initialize_parameters_zeros \n",
    "\n",
    "def initialize_parameters_zeros(layers_dims):\n",
    "    \"\"\"\n",
    "    Arguments:\n",
    "    layer_dims -- python array (list) containing the size of each layer.\n",
    "    \n",
    "    Returns:\n",
    "    parameters -- python dictionary containing your parameters \"W1\", \"b1\", ..., \"WL\", \"bL\":\n",
    "                    W1 -- weight matrix of shape (layers_dims[1], layers_dims[0])\n",
    "                    b1 -- bias vector of shape (layers_dims[1], 1)\n",
    "                    ...\n",
    "                    WL -- weight matrix of shape (layers_dims[L], layers_dims[L-1])\n",
    "                    bL -- bias vector of shape (layers_dims[L], 1)\n",
    "    \"\"\"\n",
    "    \n",
    "    parameters = {}\n",
    "    L = len(layers_dims)            # number of layers in the network\n",
    "    \n",
    "    for l in range(1, L):\n",
    "        ### START CODE HERE ### (≈ 2 lines of code)\n",
    "        parameters['W' + str(l)] = np.zeros((layers_dims[l],layers_dims[l-1]))\n",
    "        parameters['b' + str(l)] = np.zeros((layers_dims[l],1))\n",
    "        ### END CODE HERE ###\n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 = [[0. 0. 0.]\n",
      " [0. 0. 0.]]\n",
      "b1 = [[0.]\n",
      " [0.]]\n",
      "W2 = [[0. 0.]]\n",
      "b2 = [[0.]]\n"
     ]
    }
   ],
   "source": [
    "parameters = initialize_parameters_zeros([3,2,1])\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table> \n",
    "    <tr>\n",
    "    <td>\n",
    "    **W1**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 0.  0.  0.]\n",
    " [ 0.  0.  0.]]\n",
    "    </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "    <td>\n",
    "    **b1**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 0.]\n",
    " [ 0.]]\n",
    "    </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "    <td>\n",
    "    **W2**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 0.  0.]]\n",
    "    </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "    <td>\n",
    "    **b2**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 0.]]\n",
    "    </td>\n",
    "    </tr>\n",
    "\n",
    "</table> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following code to train your model on 15,000 iterations using zeros initialization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: 0.6931471805599453\n",
      "Cost after iteration 1000: 0.6931471805599453\n",
      "Cost after iteration 2000: 0.6931471805599453\n",
      "Cost after iteration 3000: 0.6931471805599453\n",
      "Cost after iteration 4000: 0.6931471805599453\n",
      "Cost after iteration 5000: 0.6931471805599453\n",
      "Cost after iteration 6000: 0.6931471805599453\n",
      "Cost after iteration 7000: 0.6931471805599453\n",
      "Cost after iteration 8000: 0.6931471805599453\n",
      "Cost after iteration 9000: 0.6931471805599453\n",
      "Cost after iteration 10000: 0.6931471805599455\n",
      "Cost after iteration 11000: 0.6931471805599453\n",
      "Cost after iteration 12000: 0.6931471805599453\n",
      "Cost after iteration 13000: 0.6931471805599453\n",
      "Cost after iteration 14000: 0.6931471805599453\n"
     ]
    },
    {
     "data": {
      "image/png": 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pf3BwcIRPJSIiRmKkh5g+BfxY0vmAqc5HnLKJZVRo67wqbzwwAzgKmAL8UNLBti+WdDjwY2AQuIrC/SdsnwacBtWFciN8LhERMQIj2oOw/WXgfwC3Ur1hv8b2v29isQE2/NQ/her+Ep19vmX7Ads3AiupAgPbp9g+1PYxVGGToT0iIrahke5BUJ9cXrHJjg9bAsyQNB34LTAb+KuOPhcCc4Cz60NJM4HV9Qnu3W3fIekQ4BDg4s3YdkREbKERB8Tmsr1e0jyqQf7GAWfaXi7pZKDf9qJ63kskrQAeBN5Th8LOVIebAO4G3mA7tziNiNiGMlhfRMQObIsH64uIiB1PAiIiIooSEBERUZSAiIiIogREREQUJSAiIqIoAREREUUJiIiIKEpAREREUQIiIiKKEhAREVGUgIiIiKIEREREFCUgIiKiKAERERFFCYiIiChKQERERFECIiIiihIQERFR1GpASJolaaWkVZLmd+lzvKQVkpZLOqfR/vG67TpJn5WkNmuNiIgNjW9rxZLGAQuAY4ABYImkRbZXNPrMAN4HHGl7naS96vbnAkcCh9RdfwS8APhBW/VGRMSG2tyDOAJYZXu17fuBhcBxHX3eBiywvQ7A9m11u4GdgQnATsCjgVtbrDUiIjq0GRCTgTWN6YG6rWkmMFPSlZKuljQLwPZVwGXAzfVjse3rWqw1IiI6tHaICSidM3Bh+zOAo4ApwA8lHQxMBJ5atwFcIun5tq/YYAPSXGAuwH777bf1Ko+IiFb3IAaAqY3pKcDaQp9v2X7A9o3ASqrAeDVwte17bd8LXAQ8u3MDtk+z3We7b9KkSa08iYiIHVWbAbEEmCFpuqQJwGxgUUefC4GjASRNpDrktBr4DfACSeMlPZrqBHUOMUVEbEOtBYTt9cA8YDHVm/t5tpdLOlnSsXW3xcAdklZQnXN4j+07gPOBXwHXAsuAZba/3VatERGxMdmdpwXGpr6+Pvf39/e6jIiIMUXSUtt9pXm5kjoiIooSEBERUZSAiIiIogREREQUJSAiIqIoAREREUUJiIiIKEpAREREUQIiIiKKEhAREVGUgIiIiKIEREREFCUgIiKiKAERERFFCYiIiChKQERERFECIiIiihIQERFRlICIiIiiBERERBS1GhCSZklaKWmVpPld+hwvaYWk5ZLOqduOlnRN4/EHSa9qs9aIiNjQ+LZWLGkcsAA4BhgAlkhaZHtFo88M4H3AkbbXSdoLwPZlwKF1nz2BVcDFbdUaEREba3MP4ghgle3Vtu8HFgLHdfR5G7DA9joA27cV1vNa4CLb97VYa0REdGgzICYDaxrTA3Vb00xgpqQrJV0taVZhPbOBc0sbkDRXUr+k/sHBwa1SdEREVNoMCBXa3DE9HpgBHAXMAc6QtPufViDtAzwdWFzagO3TbPfZ7ps0adJWKToiIiptBsQAMLUxPQVYW+jzLdsP2L4RWEkVGEOOBy6w/UCLdUZEREGbAbEEmCFpuqQJVIeKFnX0uRA4GkDSRKpDTqsb8+fQ5fBSRES0q7WAsL0emEd1eOg64DzbyyWdLOnYutti4A5JK4DLgPfYvgNA0jSqPZDL26oxIiK6k915WmBs6uvrc39/f6/LiIgYUyQttd1XmpcrqSMioigBERERRQmIiIgoSkBERERRAiIiIooSEBERUZSAiIiIogREREQUJSAiIqIoAREREUUJiIiIKEpAREREUQIiIiKKEhAREVGUgIiIiKIEREREFCUgIiKiKAERERFFCYiIiChqNSAkzZK0UtIqSfO79Dle0gpJyyWd02jfT9LFkq6r509rs9aIiNjQ+LZWLGkcsAA4BhgAlkhaZHtFo88M4H3AkbbXSdqrsYovA6fYvkTSrsBDbdUaEREba3MP4ghgle3Vtu8HFgLHdfR5G7DA9joA27cBSDoIGG/7krr9Xtv3tVhrRER0aDMgJgNrGtMDdVvTTGCmpCslXS1pVqP9LknflPQzSZ+o90giImIbaTMgVGhzx/R4YAZwFDAHOEPS7nX784B3A4cDTwLevNEGpLmS+iX1Dw4Obr3KIyKi1YAYAKY2pqcAawt9vmX7Ads3AiupAmMA+Fl9eGo9cCHwzM4N2D7Ndp/tvkmTJrXyJCIidlRtBsQSYIak6ZImALOBRR19LgSOBpA0kerQ0up62T0kDb3rvxBYQUREbDOtBUT9yX8esBi4DjjP9nJJJ0s6tu62GLhD0grgMuA9tu+w/SDV4aXvS7qW6nDV6W3VGhERG5PdeVpgbOrr63N/f3+vy4iIGFMkLbXdV5qXK6kjIqIoAREREUUJiIiIKEpAREREUQIiIiKKEhAREVGUgIiIiKIEREREFCUgIiKiaLu5klrSIHDTFqxiInD7ViqnbWOpVhhb9Y6lWmFs1TuWaoWxVe+W1Lq/7eJop9tNQGwpSf3dLjcfbcZSrTC26h1LtcLYqncs1Qpjq962as0hpoiIKEpAREREUQLiYaf1uoDNMJZqhbFV71iqFcZWvWOpVhhb9bZSa85BREREUfYgIiKiKAERERFFO3xASJolaaWkVZLm97qe4UiaKukySddJWi7pb3td06ZIGifpZ5K+0+taNkXS7pLOl/TL+jV+Tq9r6kbSSfXvwC8knStp517X1CTpTEm3SfpFo21PSZdIuqH+d49e1jikS62fqH8Pfi7pAkm797LGplK9jXnvlmRJE7fGtnbogJA0DlgAvAw4CJgj6aDeVjWs9cC7bD8VeDbwv0d5vQB/S3VP8rHgM8D3bB8IPINRWrekycDfAH22DwbGAbN7W9VGzgZmdbTNB75vewbw/Xp6NDibjWu9BDjY9iHA9cD7tnVRwzibjetF0lTgGOA3W2tDO3RAAEcAq2yvtn0/sBA4rsc1dWX7Zts/rX++h+oNbHJvq+pO0hTgFcAZva5lUyTtBjwf+CKA7ftt39XbqoY1HniMpPHALsDaHtezAdtXAHd2NB8HfKn++UvAq7ZpUV2UarV9se319eTVwJRtXlgXXV5bgE8D7wW22jePdvSAmAysaUwPMIrfcJskTQMOA/6rt5UM61+pfmEf6nUhI/AkYBA4qz4kdoakx/a6qBLbvwU+SfVJ8Wbgd7Yv7m1VI7K37Zuh+rAD7NXjekbqrcBFvS5iOJKOBX5re9nWXO+OHhAqtI367/1K2hX4BvB3tu/udT0lkl4J3GZ7aa9rGaHxwDOB/2f7MOC/GT2HQDZQH7s/DpgO7As8VtIbelvV9knS+6kO7X6117V0I2kX4P3AP27tde/oATEATG1MT2GU7ap3kvRoqnD4qu1v9rqeYRwJHCvp11SH7l4o6Su9LWlYA8CA7aE9svOpAmM0ejFwo+1B2w8A3wSe2+OaRuJWSfsA1P/e1uN6hiXpBOCVwOs9ui8YezLVh4Vl9d/bFOCnkp64pSve0QNiCTBD0nRJE6hO9C3qcU1dSRLVMfLrbP9Lr+sZju332Z5iexrV63qp7VH7Kdf2LcAaSQfUTS8CVvSwpOH8Bni2pF3q34kXMUpPqHdYBJxQ/3wC8K0e1jIsSbOAfwCOtX1fr+sZju1rbe9le1r99zYAPLP+nd4iO3RA1Ceh5gGLqf7AzrO9vLdVDetI4I1Un8avqR8v73VR25ETga9K+jlwKPDPPa6nqN7LOR/4KXAt1d/xqBoWQtK5wFXAAZIGJP018FHgGEk3UH3b5qO9rHFIl1pPBR4HXFL/nX2+p0U2dKm3nW2N7j2niIjolR16DyIiIrpLQERERFECIiIiihIQERFRlICIiIiiBERsU5J+XP87TdJfbeV1/5/Sttoi6VWStvrVq/W6721pvUdt6ci6ks6W9Nph5s+T9JYt2UaMDgmI2KZsD13xOw3YrICoR98dzgYB0dhWW94LfG5LVzKC59W6etC/reVMqtFmY4xLQMQ21fhk/FHgefVFSCfV9434hKQl9Rj8b6/7H1XfA+McqovCkHShpKX1/RDm1m0fpRrd9BpJX21uS5VP1PdOuFbS6xrr/oEevgfEV+srk5H0UUkr6lo+WXgeM4E/2r69nj5b0ucl/VDS9fVYVEP3wxjR8yps4xRJyyRdLWnvxnZe2+hzb2N93Z7LrLrtR8BrGst+SNJpki4GvjxMrZJ0av16fJfGIHul16m+8vjXko4Yye9EjF5b81NDxOaYD7zb9tAb6VyqUUkPl7QTcGX9xgXVsOwH276xnn6r7TslPQZYIukbtudLmmf70MK2XkN1ZfQzgIn1MlfU8w4DnkY1BteVwJGSVgCvBg60bZVvFnMk1ZXMTdOAF1CNjXOZpKcAb9qM59X0WOBq2++X9HHgbcA/Ffo1lZ5LP3A68EJgFfC1jmX+DPhz278f5v/gMOAA4OnA3lRDkJwpac9hXqd+4HnATzZRc4xi2YOI0eIlwJskXUM1hPkTgBn1vJ90vIn+jaRlVOP0T2306+bPgXNtP2j7VuBy4PDGugdsPwRcQ/UmfzfwB+AMSa8BSmPx7EM1PHjTebYfsn0DsBo4cDOfV9P9wNC5gqV1XZtSei4HUg3sd0M94FzngImLbP++/rlbrc/n4ddvLXBp3X+41+k2qpFmYwzLHkSMFgJOtL14g0bpKKqht5vTLwaeY/s+ST8ANnW7zdKw7kP+2Pj5QWC87fX14ZEXUQ00OI/qE3jT74HHd7R1jltjRvi8Ch5ojCD6IA//ra6n/mBXH0KaMNxz6VJXU7OGbrW+vLSOTbxOO1O9RjGGZQ8ieuUeqsHQhiwG3qlqOHMkzVT5hj2PB9bV4XAg1a1XhzwwtHyHK4DX1cfYJ1F9Iu566EPV/TYeb/s/gL+jOjzV6TrgKR1tfynpUZKeTHUDopWb8bxG6tdUh4WguidE6fk2/RKYXtcEMGeYvt1qvQKYXb9++wBH1/OHe51mAhvdMznGluxBRK/8HFhfHyo6m+p+0NOoxrEX1eGb0i0pvwe8Q9WIqyupDjMNOQ34uaSf2n59o/0C4DnAMqpPwu+1fUsdMCWPA74laWeqT9UnFfpcAXxKkhqf9FdSHb7aG3iH7T9IOmOEz2ukTq9r+wnVfZ2H2wuhrmEu8F1JtwM/Ag7u0r1brRdQ7RlcS3V/5svr/sO9TkcCH97sZxejSkZzjXiEJH0G+Lbt/5R0NvAd2+f3uKyek3QY8Pe239jrWmLL5BBTxCP3z8AuvS5iFJoI/N9eFxFbLnsQERFRlD2IiIgoSkBERERRAiIiIooSEBERUZSAiIiIov8P27KhCnuScHcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On the train set:\n",
      "Accuracy: 0.5\n",
      "On the test set:\n",
      "Accuracy: 0.5\n"
     ]
    }
   ],
   "source": [
    "parameters = model(train_X, train_Y, initialization = \"zeros\")\n",
    "print (\"On the train set:\")\n",
    "predictions_train = predict(train_X, train_Y, parameters)\n",
    "print (\"On the test set:\")\n",
    "predictions_test = predict(test_X, test_Y, parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The performance is really bad, and the cost does not really decrease, and the algorithm performs no better than random guessing. Why? Lets look at the details of the predictions and the decision boundary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predictions_train = [[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0]]\n",
      "predictions_test = [[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]\n"
     ]
    }
   ],
   "source": [
    "print (\"predictions_train = \" + str(predictions_train))\n",
    "print (\"predictions_test = \" + str(predictions_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Model with Zeros initialization\")\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-1.5,1.5])\n",
    "axes.set_ylim([-1.5,1.5])\n",
    "plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model is predicting 0 for every example. \n",
    "\n",
    "In general, initializing all the weights to zero results in the network failing to break symmetry. This means that every neuron in each layer will learn the same thing, and you might as well be training a neural network with $n^{[l]}=1$ for every layer, and the network is no more powerful than a linear classifier such as logistic regression. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='blue'>\n",
    "**What you should remember**:\n",
    "- The weights $W^{[l]}$ should be initialized randomly to break symmetry. \n",
    "- It is however okay to initialize the biases $b^{[l]}$ to zeros. Symmetry is still broken so long as $W^{[l]}$ is initialized randomly. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 - Random initialization\n",
    "\n",
    "To break symmetry, lets intialize the weights randomly. Following random initialization, each neuron can then proceed to learn a different function of its inputs. In this exercise, you will see what happens if the weights are intialized randomly, but to very large values. \n",
    "\n",
    "**Exercise**: Implement the following function to initialize your weights to large random values (scaled by \\*10) and your biases to zeros. Use `np.random.randn(..,..) * 10` for weights and `np.zeros((.., ..))` for biases. We are using a fixed `np.random.seed(..)` to make sure your \"random\" weights  match ours, so don't worry if running several times your code gives you always the same initial values for the parameters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: initialize_parameters_random\n",
    "\n",
    "def initialize_parameters_random(layers_dims):\n",
    "    \"\"\"\n",
    "    Arguments:\n",
    "    layer_dims -- python array (list) containing the size of each layer.\n",
    "    \n",
    "    Returns:\n",
    "    parameters -- python dictionary containing your parameters \"W1\", \"b1\", ..., \"WL\", \"bL\":\n",
    "                    W1 -- weight matrix of shape (layers_dims[1], layers_dims[0])\n",
    "                    b1 -- bias vector of shape (layers_dims[1], 1)\n",
    "                    ...\n",
    "                    WL -- weight matrix of shape (layers_dims[L], layers_dims[L-1])\n",
    "                    bL -- bias vector of shape (layers_dims[L], 1)\n",
    "    \"\"\"\n",
    "    \n",
    "    np.random.seed(3)               # This seed makes sure your \"random\" numbers will be the as ours\n",
    "    parameters = {}\n",
    "    L = len(layers_dims)            # integer representing the number of layers\n",
    "    \n",
    "    for l in range(1, L):\n",
    "        ### START CODE HERE ### (≈ 2 lines of code)\n",
    "        parameters['W' + str(l)] = np.random.randn(layers_dims[l],layers_dims[l-1]) * 10\n",
    "        parameters['b' + str(l)] =  np.zeros((layers_dims[l], 1))\n",
    "        ### END CODE HERE ###\n",
    "\n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 = [[ 17.88628473   4.36509851   0.96497468]\n",
      " [-18.63492703  -2.77388203  -3.54758979]]\n",
      "b1 = [[0.]\n",
      " [0.]]\n",
      "W2 = [[-0.82741481 -6.27000677]]\n",
      "b2 = [[0.]]\n"
     ]
    }
   ],
   "source": [
    "parameters = initialize_parameters_random([3, 2, 1])\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table> \n",
    "    <tr>\n",
    "    <td>\n",
    "    **W1**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 17.88628473   4.36509851   0.96497468]\n",
    " [-18.63492703  -2.77388203  -3.54758979]]\n",
    "    </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "    <td>\n",
    "    **b1**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 0.]\n",
    " [ 0.]]\n",
    "    </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "    <td>\n",
    "    **W2**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[-0.82741481 -6.27000677]]\n",
    "    </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "    <td>\n",
    "    **b2**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 0.]]\n",
    "    </td>\n",
    "    </tr>\n",
    "\n",
    "</table> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following code to train your model on 15,000 iterations using random initialization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\Desktop\\Improving-Deep-Neural-Networks\\week1\\Initialization\\init_utils.py:145: RuntimeWarning: divide by zero encountered in log\n",
      "  logprobs = np.multiply(-np.log(a3),Y) + np.multiply(-np.log(1 - a3), 1 - Y)\n",
      "C:\\Users\\Administrator\\Desktop\\Improving-Deep-Neural-Networks\\week1\\Initialization\\init_utils.py:145: RuntimeWarning: invalid value encountered in multiply\n",
      "  logprobs = np.multiply(-np.log(a3),Y) + np.multiply(-np.log(1 - a3), 1 - Y)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: inf\n",
      "Cost after iteration 1000: 0.6250982793959966\n",
      "Cost after iteration 2000: 0.5981216596703697\n",
      "Cost after iteration 3000: 0.5638417572298645\n",
      "Cost after iteration 4000: 0.5501703049199763\n",
      "Cost after iteration 5000: 0.5444632909664456\n",
      "Cost after iteration 6000: 0.5374513807000807\n",
      "Cost after iteration 7000: 0.4764042074074983\n",
      "Cost after iteration 8000: 0.39781492295092263\n",
      "Cost after iteration 9000: 0.3934764028765484\n",
      "Cost after iteration 10000: 0.3920295461882659\n",
      "Cost after iteration 11000: 0.38924598135108\n",
      "Cost after iteration 12000: 0.3861547485712325\n",
      "Cost after iteration 13000: 0.384984728909703\n",
      "Cost after iteration 14000: 0.3827828308349524\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On the train set:\n",
      "Accuracy: 0.83\n",
      "On the test set:\n",
      "Accuracy: 0.86\n"
     ]
    }
   ],
   "source": [
    "parameters = model(train_X, train_Y, initialization = \"random\")\n",
    "print (\"On the train set:\")\n",
    "predictions_train = predict(train_X, train_Y, parameters)\n",
    "print (\"On the test set:\")\n",
    "predictions_test = predict(test_X, test_Y, parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you see \"inf\" as the cost after the iteration 0, this is because of numerical roundoff; a more numerically sophisticated implementation would fix this. But this isn't worth worrying about for our purposes. \n",
    "\n",
    "Anyway, it looks like you have broken symmetry, and this gives better results. than before. The model is no longer outputting all 0s. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 0 1 1 0 0 1 1 1 1 1 0 1 0 0 1 0 1 1 0 0 0 1 0 1 1 1 1 1 1 0 1 1 0 0 1\n",
      "  1 1 1 1 1 1 1 0 1 1 1 1 0 1 0 1 1 1 1 0 0 1 1 1 1 0 1 1 0 1 0 1 1 1 1 0\n",
      "  0 0 0 0 1 0 1 0 1 1 1 0 0 1 1 1 1 1 1 0 0 1 1 1 0 1 1 0 1 0 1 1 0 1 1 0\n",
      "  1 0 1 1 0 0 1 0 0 1 1 0 1 1 1 0 1 0 0 1 0 1 1 1 1 1 1 1 0 1 1 0 0 1 1 0\n",
      "  0 0 1 0 1 0 1 0 1 1 1 0 0 1 1 1 1 0 1 1 0 1 0 1 1 0 1 0 1 1 1 1 0 1 1 1\n",
      "  1 0 1 0 1 0 1 1 1 1 0 1 1 0 1 1 0 1 1 0 1 0 1 1 1 0 1 1 1 0 1 0 1 0 0 1\n",
      "  0 1 1 0 1 1 0 1 1 0 1 1 1 0 1 1 1 1 0 1 0 0 1 1 0 1 1 1 0 0 0 1 1 0 1 1\n",
      "  1 1 0 1 1 0 1 1 1 0 0 1 0 0 0 1 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 1 1 1\n",
      "  1 1 1 1 0 0 0 1 1 1 1 0]]\n",
      "[[1 1 1 1 0 1 0 1 1 0 1 1 1 0 0 0 0 1 0 1 0 0 1 0 1 0 1 1 1 1 1 0 0 0 0 1\n",
      "  0 1 1 0 0 1 1 1 1 1 0 1 1 1 0 1 0 1 1 0 1 0 1 0 1 1 1 1 1 1 1 1 1 0 1 0\n",
      "  1 1 1 1 1 0 1 0 0 1 0 0 0 1 1 0 1 1 0 0 0 1 1 0 1 1 0 0]]\n"
     ]
    }
   ],
   "source": [
    "print (predictions_train)\n",
    "print (predictions_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Model with large random initialization\")\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-1.5,1.5])\n",
    "axes.set_ylim([-1.5,1.5])\n",
    "plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**:\n",
    "- The cost starts very high. This is because with large random-valued weights, the last activation (sigmoid) outputs results that are very close to 0 or 1 for some examples, and when it gets that example wrong it incurs a very high loss for that example. Indeed, when $\\log(a^{[3]}) = \\log(0)$, the loss goes to infinity.\n",
    "- Poor initialization can lead to vanishing/exploding gradients, which also slows down the optimization algorithm. \n",
    "- If you train this network longer you will see better results, but initializing with overly large random numbers slows down the optimization.\n",
    "\n",
    "<font color='blue'>\n",
    "**In summary**:\n",
    "- Initializing weights to very large random values does not work well. \n",
    "- Hopefully intializing with small random values does better. The important question is: how small should be these random values be? Lets find out in the next part! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 - He initialization\n",
    "\n",
    "Finally, try \"He Initialization\"; this is named for the first author of He et al., 2015. (If you have heard of \"Xavier initialization\", this is similar except Xavier initialization uses a scaling factor for the weights $W^{[l]}$ of `sqrt(1./layers_dims[l-1])` where He initialization would use `sqrt(2./layers_dims[l-1])`.)\n",
    "\n",
    "**Exercise**: Implement the following function to initialize your parameters with He initialization.\n",
    "\n",
    "**Hint**: This function is similar to the previous `initialize_parameters_random(...)`. The only difference is that instead of multiplying `np.random.randn(..,..)` by 10, you will multiply it by $\\sqrt{\\frac{2}{\\text{dimension of the previous layer}}}$, which is what He initialization recommends for layers with a ReLU activation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: initialize_parameters_he\n",
    "\n",
    "def initialize_parameters_he(layers_dims):\n",
    "    \"\"\"\n",
    "    Arguments:\n",
    "    layer_dims -- python array (list) containing the size of each layer.\n",
    "    \n",
    "    Returns:\n",
    "    parameters -- python dictionary containing your parameters \"W1\", \"b1\", ..., \"WL\", \"bL\":\n",
    "                    W1 -- weight matrix of shape (layers_dims[1], layers_dims[0])\n",
    "                    b1 -- bias vector of shape (layers_dims[1], 1)\n",
    "                    ...\n",
    "                    WL -- weight matrix of shape (layers_dims[L], layers_dims[L-1])\n",
    "                    bL -- bias vector of shape (layers_dims[L], 1)\n",
    "    \"\"\"\n",
    "    \n",
    "    np.random.seed(3)\n",
    "    parameters = {}\n",
    "    L = len(layers_dims) - 1 # integer representing the number of layers\n",
    "     \n",
    "    for l in range(1, L + 1):\n",
    "        ### START CODE HERE ### (≈ 2 lines of code)\n",
    "        parameters['W' + str(l)] = np.random.randn(layers_dims[l],layers_dims[l-1]) * np.sqrt(2./layers_dims[l-1])\n",
    "        parameters['b' + str(l)] = np.zeros((layers_dims[l], 1))\n",
    "        ### END CODE HERE ###\n",
    "        \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 = [[ 1.78862847  0.43650985]\n",
      " [ 0.09649747 -1.8634927 ]\n",
      " [-0.2773882  -0.35475898]\n",
      " [-0.08274148 -0.62700068]]\n",
      "b1 = [[0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]]\n",
      "W2 = [[-0.03098412 -0.33744411 -0.92904268  0.62552248]]\n",
      "b2 = [[0.]]\n"
     ]
    }
   ],
   "source": [
    "parameters = initialize_parameters_he([2, 4, 1])\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table> \n",
    "    <tr>\n",
    "    <td>\n",
    "    **W1**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 1.78862847  0.43650985]\n",
    " [ 0.09649747 -1.8634927 ]\n",
    " [-0.2773882  -0.35475898]\n",
    " [-0.08274148 -0.62700068]]\n",
    "    </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "    <td>\n",
    "    **b1**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 0.]\n",
    " [ 0.]\n",
    " [ 0.]\n",
    " [ 0.]]\n",
    "    </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "    <td>\n",
    "    **W2**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[-0.03098412 -0.33744411 -0.92904268  0.62552248]]\n",
    "    </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "    <td>\n",
    "    **b2**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 0.]]\n",
    "    </td>\n",
    "    </tr>\n",
    "\n",
    "</table> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following code to train your model on 15,000 iterations using He initialization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: 0.8830537463419761\n",
      "Cost after iteration 1000: 0.6879825919728063\n",
      "Cost after iteration 2000: 0.6751286264523371\n",
      "Cost after iteration 3000: 0.6526117768893807\n",
      "Cost after iteration 4000: 0.6082958970572937\n",
      "Cost after iteration 5000: 0.5304944491717495\n",
      "Cost after iteration 6000: 0.4138645817071793\n",
      "Cost after iteration 7000: 0.3117803464844441\n",
      "Cost after iteration 8000: 0.23696215330322556\n",
      "Cost after iteration 9000: 0.18597287209206828\n",
      "Cost after iteration 10000: 0.15015556280371808\n",
      "Cost after iteration 11000: 0.12325079292273548\n",
      "Cost after iteration 12000: 0.09917746546525937\n",
      "Cost after iteration 13000: 0.08457055954024274\n",
      "Cost after iteration 14000: 0.07357895962677366\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On the train set:\n",
      "Accuracy: 0.9933333333333333\n",
      "On the test set:\n",
      "Accuracy: 0.96\n"
     ]
    }
   ],
   "source": [
    "parameters = model(train_X, train_Y, initialization = \"he\")\n",
    "print (\"On the train set:\")\n",
    "predictions_train = predict(train_X, train_Y, parameters)\n",
    "print (\"On the test set:\")\n",
    "predictions_test = predict(test_X, test_Y, parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Model with He initialization\")\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-1.5,1.5])\n",
    "axes.set_ylim([-1.5,1.5])\n",
    "plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**:\n",
    "- The model with He initialization separates the blue and the red dots very well in a small number of iterations.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 5 - Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "You have seen three different types of initializations. For the same number of iterations and same hyperparameters the comparison is:\n",
    "\n",
    "<table> \n",
    "    <tr>\n",
    "        <td>\n",
    "        **Model**\n",
    "        </td>\n",
    "        <td>\n",
    "        **Train accuracy**\n",
    "        </td>\n",
    "        <td>\n",
    "        **Problem/Comment**\n",
    "        </td>\n",
    "\n",
    "    </tr>\n",
    "        <td>\n",
    "        3-layer NN with zeros initialization\n",
    "        </td>\n",
    "        <td>\n",
    "        50%\n",
    "        </td>\n",
    "        <td>\n",
    "        fails to break symmetry\n",
    "        </td>\n",
    "    <tr>\n",
    "        <td>\n",
    "        3-layer NN with large random initialization\n",
    "        </td>\n",
    "        <td>\n",
    "        83%\n",
    "        </td>\n",
    "        <td>\n",
    "        too large weights \n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "        3-layer NN with He initialization\n",
    "        </td>\n",
    "        <td>\n",
    "        99%\n",
    "        </td>\n",
    "        <td>\n",
    "        recommended method\n",
    "        </td>\n",
    "    </tr>\n",
    "</table> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='blue'>\n",
    "**What you should remember from this notebook**:\n",
    "- Different initializations lead to different results\n",
    "- Random initialization is used to break symmetry and make sure different hidden units can learn different things\n",
    "- Don't intialize to values that are too large\n",
    "- He initialization works well for networks with ReLU activations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "coursera": {
   "course_slug": "deep-neural-network",
   "graded_item_id": "XOESP",
   "launcher_item_id": "8IhFN"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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